As one of the most successful applications of image analysis and understanding, face
recognition has recently received significant attention, especially during the past several …

Face recognition is a rapidly growing research area due to increasing demands for security
in commercial and law enforcement applications. This paper provides an up-to-date review …

We consider the problem of automatically recognizing human faces from frontal views with
varying expression and illumination, as well as occlusion and disguise. We cast the …

We present a generative appearance-based method for recognizing human faces under
variation in lighting and viewpoint. Our method exploits the fact that the set of images of an …

In this paper, we present a sparse correntropy framework for computing robust sparse
representations of face images for recognition. Compared with the state-of-the-art l 1 norm …

Recently, regression analysis has become a popular tool for face recognition. Most existing
regression methods use the one-dimensional, pixel-based error model, which characterizes …

Low-dimensional feature representation with enhanced discriminatory power is of
paramount importance to face recognition (FR) systems. Most of traditional linear …

A general and efficient design approach using a radial basis function (RBF) neural classifier
to cope with small training sets of high dimension, which is a problem frequently …

One of the main challenges faced by the current face recognition techniques lies in the
difficulties of collecting samples. Fewer samples per person mean less laborious effort for …

Techniques that can introduce low-dimensional feature representation with enhanced
discriminatory power is of paramount importance in face recognition (FR) systems. It is well …